Browsing by Author "Santoni, B. G."

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    Development and biocompatibility characterization of a biomems sensor for monitoring the progression of fracture healing
    (2009-06) Santoni, B. G.; Melik, R.; Ünal, Emre; Perkgöz, N. K.; Kamstock, D. A.; Ryan, S. D.; Dernell W. S.; Demir, Hilmi Volkan; Puttlitz, C. M.
    Orthopaedic extremity injuries present a large medical and financial burden to the United States and world-wide communities [1]. Approximately six million long bone fractures are reported annually in the United States and approximately 10% of these fractures do not heal properly. Though the exact mechanism of impaired healing is poorly understood, many of these non-unions result when there is a communited condition that does not proceed through a stabilized healing pathway [2]. Currently, clinicians may monitor healing visually by radiographs, or via manual manipulation of the bone at the fracture [3]. Unfortunately, the course of aberrant fracture healing is not easily diagnosed in the early period when standard radiographic information of the fracture is not capable of discriminating the healing pathway. Manual assessment of fracture healing is also an inadequate diagnostic tool in the early stages of healing [4].
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    Implantable microelectromechanical sensors for diagnostic monitoring and post-surgical prediction of bone fracture healing
    (John Wiley and Sons Inc., 2015) McGilvray, K. C.; Ünal, E.; Troyer, K. L.; Santoni, B. G.; Palmer, R. H.; Easley, J. T.; Demir, Hilmi Volkan; Puttlitz, C. M.
    The relationship between modern clinical diagnostic data, such as from radiographs or computed tomography, and the temporal biomechanical integrity of bone fracture healing has not been well-established. A diagnostic tool that could quantitatively describe the biomechanical stability of the fracture site in order to predict the course of healing would represent a paradigm shift in the way fracture healing is evaluated. This paper describes the development and evaluation of a wireless, biocompatible, implantable, microelectromechanical system (bioMEMS) sensor, and its implementation in a large animal (ovine) model, that utilized both normal and delayed healing variants. The in vivo data indicated that the bioMEMS sensor was capable of detecting statistically significant differences (p-value <0.04) between the two fracture healing groups as early as 21 days post-fracture. In addition, post-sacrifice micro-computed tomography, and histology data demonstrated that the two model variants represented significantly different fracture healing outcomes, providing explicit supporting evidence that the sensor has the ability to predict differential healing cascades. These data verify that the bioMEMS sensor can be used as a diagnostic tool for detecting the in vivo course of fracture healing in the acute post-treatment period. © 2015 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.